公开(公告)号：US20240404296A1

公开(公告)日：2024-12-05

申请号：US18327643

申请日：2023-06-01

Applicant: NVIDIA Corporation

Inventor： Shagan Sah ,  Niranjan Avadhanam ,  Rajath Shetty ,  Ratin Kumar ,  Yile Chen

IPC: G06V20/58 ,  G06T7/20 ,  G06T7/50 ,  G06V10/764 ,  G06V20/59 ,  G08B13/196

Abstract: In various examples, low power proximity based threat detection using optical flow for vehicle systems and applications are provided. Some embodiments may use a tiered framework that uses sensor fusion techniques to detect and track the movement of a threat candidate, and perform a threat classification and/or intent prediction as the threat candidate approaches approach. Relative depth indications from optical flow, computed using data from image sensors, can be used to initially segment and track a moving object over a sequence of image frames. Additional sensors and processing may be brought online when a moving object becomes close enough to be considered a higher risk threat candidate. A threat response system may generate a risk score based on a predicted intent of a threat candidate, and when the risk score exceeds a certain threshold, then the threat response system may respond accordingly based on the threat classification and/or risk score.

公开(公告)号：US20240371136A1

公开(公告)日：2024-11-07

申请号：US18771646

申请日：2024-07-12

Applicant: NVIDIA Corporation

Inventor： Sakthivel Sivaraman ,  Shagan Sah ,  Niranjan Avadhanam

IPC: G06V10/764 ,  G06F18/20 ,  G06F18/2321 ,  G06N3/045 ,  G06V20/40 ,  G06V40/10 ,  G06V40/20

Abstract: In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals-such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter.

公开(公告)号：US12073604B2

公开(公告)日：2024-08-27

申请号：US18333281

申请日：2023-06-12

Applicant: NVIDIA Corporation

Inventor： Sakthivel Sivaraman ,  Shagan Sah ,  Niranjan Avadhanam

IPC: G06V10/00 ,  G06F18/20 ,  G06F18/2321 ,  G06N3/045 ,  G06V10/764 ,  G06V20/40 ,  G06V40/10 ,  G06V40/20

CPC classification number: G06V10/764 ,  G06F18/2321 ,  G06F18/285 ,  G06N3/045 ,  G06V20/47 ,  G06V40/113 ,  G06V40/28

Abstract: In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter.

公开(公告)号：US20240034260A1

公开(公告)日：2024-02-01

申请号：US18481603

申请日：2023-10-05

Applicant: NVIDIA Corporation

Inventor： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC: B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  G06N3/02 ,  B60W50/14

CPC classification number: B60R21/017 ,  B60R21/013 ,  B60W60/005 ,  G06N3/02 ,  B60W50/14 ,  B60W2050/0062

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US11851014B2

公开(公告)日：2023-12-26

申请号：US17939613

申请日：2022-09-07

Applicant: NVIDIA Corporation

Inventor： Atousa Torabi ,  Sakthivel Sivaraman ,  Niranjan Avadhanam ,  Shagan Sah

IPC: B60R21/017 ,  B60R21/013 ,  B60W60/00 ,  G06N3/02 ,  B60W50/14 ,  B60W50/00 ,  B60R21/01

CPC classification number: B60R21/017 ,  B60R21/013 ,  B60W50/14 ,  B60W60/005 ,  G06N3/02 ,  B60R2021/01211 ,  B60R2021/01286 ,  B60W2050/0062

Abstract: In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

公开(公告)号：US20230297074A1

公开(公告)日：2023-09-21

申请号：US17697566

申请日：2022-03-17

Applicant: Nvidia Corporation

Inventor： Christopher Jason Paxton ,  Shagan Sah ,  Ratin Kumar ,  Dieter Fox

IPC: G05B19/4155 ,  B25J13/00 ,  B25J9/16 ,  B25J13/08

CPC classification number: G05B19/4155 ,  B25J13/003 ,  B25J9/1661 ,  B25J13/08 ,  G05B2219/50391 ,  G05B2219/40269

Abstract: Approaches provide for performance of a complex (e.g., compound) task that may involve multiple discrete tasks not obvious from an instruction to perform the complex task. A set of conditions for an environment can be determined using captured image data, and the instruction analyzed to determine a set of final conditions to exist in the environment after performance of the instruction. These initial and end conditions are used to determine a sequence of discrete tasks to be performed to cause a robot or automated device to perform the instruction. This can involve use of a symbolic or visual planner in at least some embodiments, as well as a search of possible sequences of actions available for the robot or automated device. A robot can be caused to perform the sequence of discrete tasks, and feedback provided such that the sequence of tasks can be modified as appropriate.

公开(公告)号：US11487968B2

公开(公告)日：2022-11-01

申请号：US17004252

申请日：2020-08-27

Applicant: NVIDIA Corporation

Inventor： Nuri Murat Arar ,  Niranjan Avadhanam ,  Nishant Puri ,  Shagan Sah ,  Rajath Shetty ,  Sujay Yadawadkar ,  Pavlo Molchanov

IPC: G06K9/00 ,  G06K9/62 ,  G06N20/00 ,  G06V10/94 ,  G06V20/59 ,  G06V20/64 ,  G06V40/16 ,  G06V40/18

Abstract: Systems and methods for more accurate and robust determination of subject characteristics from an image of the subject. One or more machine learning models receive as input an image of a subject, and output both facial landmarks and associated confidence values. Confidence values represent the degrees to which portions of the subject's face corresponding to those landmarks are occluded, i.e., the amount of uncertainty in the position of each landmark location. These landmark points and their associated confidence values, and/or associated information, may then be input to another set of one or more machine learning models which may output any facial analysis quantity or quantities, such as the subject's gaze direction, head pose, drowsiness state, cognitive load, or distraction state.